马尔可夫调制网络的随机优化及其在时延约束无线调度中的应用
Stochastic Optimization for Markov Modulated Networks with Application to Delay Constrained Wireless Scheduling
AI总结：
针对混合时延约束用户与无时延约束用户的无线系统，提出一种调度算法，通过将约束优化转化为加权随机最短路径问题，最大化吞吐量效用、稳定队列并满足时延约束。
摘要：
我们考虑一个无线系统，其中包含少量有时延约束的用户和大量无时延约束的用户。我们开发了一种调度算法，该算法对时变信道做出反应，最大化吞吐量效用（在期望范围内），稳定所有队列，并满足时延约束。通过将约束优化简化为一组加权随机最短路径问题来解决该问题，这些问题作为马尔可夫决策网络中最大权重策略的自然推广。我们还给出了相应最短路径问题的近似结果，并讨论了与无时延约束系统相比所增加的复杂性和时延。该求解技术具有通用性，适用于其他受约束的随机决策问题。
英文摘要：
We consider a wireless system with a small number of delay constrained users and a larger number of users without delay constraints. We develop a scheduling algorithm that reacts to time varying channels and maximizes throughput utility (to within a desired proximity), stabilizes all queues, and satisfies the delay constraints. The problem is solved by reducing the constrained optimization to a set of weighted stochastic shortest path problems, which act as natural generalizations of max-weight policies to Markov decision networks. We also present approximation results for the corresponding shortest path problems, and discuss the additional complexity and delay incurred as compared to systems without delay constraints. The solution technique is general and applies to other constrained stochastic decision problems.
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